Data Quality Associate Analyst

Why this role exists: The Data Quality Associate Analyst ensures the integrity, accuracy, and reliability of the data assets within the Officeworks enterprise data platform, Snowflake. By defining and implementing robust data quality rules and monitoring frameworks, the role enables the transition toward a democratised, self-serve analytics model and supports the successful deployment of AI and Machine Learning initiatives. The role plays a critical part in building trust in data across the Technology function and the broader business, helping Officeworks make informed, data-led decisions.

Where you will make a difference: In this role you will: Data Quality Framework & Rule Definition Define and implement comprehensive data quality rules & standards using Snowflake and specialised DQ tools. Collaborate with business and engineering teams to identify critical data elements and quality requirements. Establish automated native checks and validation logic within data pipelines.

Monitoring & Issue Resolution Proactively monitor data quality across all pipelines and downstream systems to identify anomalies. Investigate the root cause of data quality issues and work cross-functionally to drive remediation. Support and maintain data quality tooling, such as Alation DQ, to ensure continuous oversight.

Reporting & Insights Develop and maintain dashboards to report on key data quality metrics and trends. Provide regular updates to stakeholders on the health of the enterprise data estate. Track the progress of data cleansing activities and their impact on business outcomes.

Continuous Improvement & Innovation Identify opportunities to automate manual data validation processes and enhance DQ maturity. Stay abreast of emerging DQ methodologies, particularly as they relate to the leap from ML to AI. Drive process improvements within the Data & Analytics hub to streamline data operations and documentation standards.

Collaboration & Capability Assist with broader data engineering or modelling tasks as required to support team delivery. Contribute to the documentation of data lineage and metadata to ensure institutional knowledge is captured. Who you will be working with: Data & AI Team: Collaborate with Data Architects, Modellers, and Engineers to embed quality at the source.

Enterprise Data Architect and Data Governance lead. Analytics Teams: Partner with Data Scientists and Analysts to ensure high-quality training data for machine learning systems. Business Stakeholders: Engage with functional partners to understand reporting requirements and resolve data discrepancies.

External Partners: Liaise with technology vendors and delivery partners to align quality standards across the ecosystem. What success looks like: Data Trust: Measurable improvement in data accuracy and reliability across the Snowflake platform. Operational Efficiency: Reduction in the time taken to identify and resolve data quality incidents through automation.

Stakeholder Confidence: High levels of business satisfaction with the quality and availability of self-serve datasets. Continuous Improvement: Evidence of implemented process enhancements within the Data & AI team. How you will lead: Individual Contributor Lives our Officeworks values and behaviours Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour Operates in line with applicable Officeworks company policies and Code of Conduct Demonstrates a strong sense of personal accountability and curiosity to learn and develop Qualifications and work experience: Essential Education: Tertiary qualification in Data Science, Computer Science, Information Technology, or a related field.

Experience: 3+ years of experience in Data Quality, Data Analysis, or Data Engineering roles within a complex corporate environment. Technical Skills: Proven proficiency in Snowflake and SQL for data extraction, profiling, and validation. Tools: Hands-on experience with Data Quality or Data Cataloguing tools (e.g., Alation, Great Expectations, or native Snowflake DQ checks).

Strong understanding of data pipeline architectures. Demonstrated ability to adapt to new technologies and understand the shifting requirements between traditional data and AI. Preferred Industry Context: Experience working within a retail or omnichannel environment.

Systems: Familiarity with GitHub for version control and SAP BW/Datasphere environments. Process Improvement: Experience in applying Lean or Continuous Improvement methodologies to data operations.